The configurations of the backbone are shown in Fig. 3. To extract multi-scale features to overcome the scale variations of the crowd, firstly, four different receptive fields are inserted into our system at the beginning of this backbone. Each of them has 16 filters. The input image is mapped at different scales by them synchronously, then the results are fed to the following modules. Secondly, inside this module, we design several 2x2 max-pooling layers to extract multi-scale features. Thirdly, we fuse the feature maps with complementary information from different depths of convolutional layers. Every convolutional layer is followed by ReLU. In order to increase the depth of the network to enhance its learning ability without introducing too many parameters, the parameters of the shared module are shared to alleviate overfitting caused by excessive parameters. Since the dilated convolution [1]} can increase the receptive field with fewer parameters, the dilated convolution is widely used in our SegCrowdNet.
